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Final archive: v17a compact — experiment finished
Analytically constructed weights — no training. Useful, open-ended chat
was not achieved under the experiment's constraints. The research is finished;
this release publishes a compact representation of the same final checkpoint.
The packed weights occupy exactly 1,528,592 bytes, storing 995,905 nonzero
coefficients. The loader reconstructs individual tensors when needed and
retains 12,952,916 bytes of weight tensors after inference. Temporary tensors,
activations and runtime overhead require additional RAM/VRAM. The architecture
and compute dimensions remain unchanged.
Every float32 bit reconstructs exactly, including six negative-zero entries.
There is no quantization, coefficient update, training, new grammar or added
capability. The original dense model and earlier failed candidates remain in
the historical releases.
The attached direct-weight-chat-v17a-compact.zip contains the compact model,
matching PyTorch runtime, frozen compiler source and local chat UI. Extract it
to a new directory and run with PyTorch already installed:
python -m chat1gb_autoregressive.compact_chat --model-dir compact --device auto/reset clears context; /quit exits. The model supports narrow fact/correction
grammar, bounded signed addition/subtraction and short ground-rule chains. It
does not support useful general chat, creative writing, code generation,
general mathematics or searching alternative proofs. The alternative-proof
baseline remains 29/104, with all failures preserved.
Verification preserves all text, token IDs and EOS decisions in 6,371 CUDA
cases and 788 CPU cases, including the existing failures. Independent archive
reconstruction matches the frozen original weight hash exactly.
The compact verification guide
links exact size, round-trip hash, CPU/CUDA response parity and memory evidence.
Every historical version's results
and the construction claim audit
remain public.
The same compact weights and matching source are available on
Hugging Face.
This is a custom offline PyTorch runtime, not a Transformers AutoModel or GGUF
checkpoint. Checksums are attached as SHA256SUMS.txt.